Model

This section provides a practical overview of core model families you should know for ML interviews, plus how to reason about trade‑offs.

Key topics covered here:

• Bias vs. variance and error decomposition
• Regularization: L1/L2, dropout, early stopping
• Model selection: baselines, ensembling, interpretability

Use the pages below for focused refreshers on each model class.

Pages

• Model Overview
• Linear and Logistic Regression
• Bayes' Theorem and Naive Bayes
• k-Means Clustering
• Principal Component Analysis (PCA)
• Support Vector Machines
• Tree-based Methods
• Ensemble Learning